Fabrice Denis, Ethan Basch, Anne-Lise Septans, Jaafar Bennouna, Thierry Urban, Amylou C. Dueck & Christophe Letellier
Two-year survival comparing web-based symptom monitoring vs routine surveillance following treatment for lung cancer,
JAMA. 321 (3), 306-307, 2019. Online
Symptom monitoring during chemotherapy via web-based patient-reported outcomes (PROs) was previously demonstrated to lengthen survival in a single-center study.1 A multicenter randomized clinical trial compared web-based monitoring vs standard scheduled imaging to detect symptomatic recurrence in patients with lung cancer following initial treatment. A planned interim analysis (9-month follow-up) found a significant survival benefit (19-month survival in the PRO group vs 12 months in the control group).2 We now present the final overall survival analysis.
I. Sendiña-Nadal & C. Letellier
Observability of dynamical networks from graphic and symbolic approaches
In Springer Proceedings in Complexity, X S. Cornelius, C. Granell Martorell, J. Gómez-Gardeñes & B. Gonçalves (eds) CompleNet 2019. Online or in ArXiv
A dynamical network, a graph whose nodes are dynamical systems, is usually characterized by a large dimensional space which is not always accessible due to the impossibility of measuring all the variables spanning the state space. Therefore, it is of the utmost importance to determine a reduced set of variables providing all the required information to non-ambiguously distinguish its different states. Inherited from control theory, one possible approach is based on the use of the observability matrix defined as the Jacobian matrix of the change of coordinates between the original state space and the space reconstructed from the measured variables. The observability of a given system can be accurately assessed by symbolically computing the complexity of the determinant of the observability matrix and quantified by symbolic observability coefficients. In this work, we extend the symbolic observability, previously developed for dynamical systems, to networks made of coupled d-dimensional node dynamics (d > 1). From the observability of the node dynamics, the coupling function between the nodes, and the adjacency matrix, it is indeed possible to construct the observability of a large network with an arbitrary topology.
C. Letellier, I. Leyva & I. Sendiña-Nadal
A novel complexity measure to distinguish organized from disorganized dynamics
We propose a new metric to characterize the complex behavior of a dynamical system and to distinguish between organized and disorganized complexity. The approach combines two quantities that separately assess the degree of unpredictability and the lack of structure in the Poincaré plane constructed from a given time series. As for the former, we use the permutation entropy Sp, while for the later, we introduce a new indicator, the structurality Δ, which accounts for the fraction of visited points in the Poincaré plane. The complexity measure thus defined as the sum of those two components is validated by classifying in the (Sp,Δ) space the complexity of several benchmark dissipative and conservative dynamical systems. As an application, we show how the new metric can be used as a powerful biomarker for different cardiac pathologies.